U.S. patent number 6,415,174 [Application Number 09/434,503] was granted by the patent office on 2002-07-02 for ecg derived respiratory rhythms for improved diagnosis of sleep apnea.
This patent grant is currently assigned to Board of Regents the University of Texas System. Invention is credited to Khosrow Bebehani, John R. Burk, Edgar A. Lucas.
United States Patent |
6,415,174 |
Bebehani , et al. |
July 2, 2002 |
**Please see images for:
( Certificate of Correction ) ** |
ECG derived respiratory rhythms for improved diagnosis of sleep
apnea
Abstract
Respiratory rhythms of a subject are derived from measured ECG
signals utilizing leads placed for significant influence of chest
movement on the ECG signals. The QRS pulses within ECG signals
measured in two substantially orthogonal planes are located by
applying a sequence of filters. The knot and period of the QRS
pulses are then determined, with adjustments made during a learning
phase of data sampling. The QRS pulse areas in both planes is then
calculated. These pulse areas are employed to determine the angle
of orientation of the depolarization wave's mean electrical axis
(MEA) at the QRS pulse locations. Cubic spline interpolation of the
data points for the MEA angle provides a smooth breathing curve,
which may be scored for sleep disordered breathing events. The
ECG-derived respiratory (EDR) signal may be employed in lieu of
airflow measurements where such measurements are not available, or
may be employed in conjunction with airflow measurements and/or
measured cardiac activity data to discriminate between arrhythmias
associated with disordered breathing versus those associated with
cardiac malfunction, reducing misdiagnosis. The additional
processing of ECG data required for derivation of respiratory
rhythms may be easily automated and implemented at nominal
incremental cost per unit.
Inventors: |
Bebehani; Khosrow (Arlington,
TX), Burk; John R. (Aledo, TX), Lucas; Edgar A. (Fort
Worth, TX) |
Assignee: |
Board of Regents the University of
Texas System (Arlington, TX)
|
Family
ID: |
26804922 |
Appl.
No.: |
09/434,503 |
Filed: |
November 5, 1999 |
Current U.S.
Class: |
600/513;
600/512 |
Current CPC
Class: |
A61B
5/349 (20210101); A61B 5/0816 (20130101); A61B
5/316 (20210101); A61B 5/113 (20130101); A61B
5/7239 (20130101) |
Current International
Class: |
A61B
5/0452 (20060101); A61B 5/11 (20060101); A61B
5/113 (20060101); A61B 005/040 () |
Field of
Search: |
;600/508,509,512,517,523 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
F Pinciroli, R. Rossi, L. Vergani, P. Carnevali, S. Mantero and O.
Parigi, Construction of Respiratory Waveforms, Sep. 17, 1985, pp.
391-409. .
F. Pinciroli, R. Rossi and L. Vergani, Detection of Electrical Axis
Variation for the Extraction of Respiratory Information, 1985,
IEEE, pp. 499-502. .
George B. Moody, Roger G. Mark, Andrea Zoccola, and Sara Mantero,
Derivation of Respiratory Signals from Multi-Lead ECGS, 1985 IEEE,
pp. 113-116. .
George B. Moody, Roger G. Mark, Marjorie A. Bump, Joseph S.
Weinstein, Aaron D. Berman, Joseph E. Mietus, and Ary L.
Goldberger, Clinical Validation of the ECG-Derived Respiration
(EDR) Technique, 1987, IEEE, pp. 507-510. .
S. S. Reisman and S. Yang, An Algorithm for Beat Detection and
Classification in Exercise ECGS, 1987, IEEE, pp. 663-666. .
A.M. Bianchi, U.J. Scholz, L.T. Mainardi, P. Orlandini, C. Pozza
and S. Cerutti, Extraction of the Respiratory Influence From the
Heart Rate Variability Signal by Means of Lattice Adaptive Filter.
.
Lingeng Zhao, Stanley Reisman and Thomas Findley, Respiration
Derived from the Electrocardiogram During Heart Rate Variability
Studies. .
David Caggiano and Stanley Reisman, Respiration Derived from the
Electrocardiogram: A Quantitative Comparison of Three Different
Methods, 1996, IEEE, pp. 103-104. .
Prameela Ray, An Investigation of Respiratory Signal Derived from
Electrocardiography Signals for Detection of Sleep Apnea, The
University of Texas at Arlington, Dec. 1996..
|
Primary Examiner: Jastrzab; Jeffrey R.
Attorney, Agent or Firm: Bracewell & Patterson, LLP
Parent Case Text
This application claims priority from provisional application Ser.
No. 60/107,564, filed Nov. 9, 1998.
Claims
What is claimed is:
1. A method for measuring body functions, comprising the following
steps:
(a) placing ECG electrodes on a patient's body in pre-determined
locations;
(b) measuring ECG signals in two planes, as a first lead and a
second lead;
(c) locating QRS pulses within measured ECG signals;
(d) calculating an area of the QRS pulses from measured values of
the ECG signals;
(e) calculating an angle of mean electrical axis; and
(f) modeling an EDR signal by interpolation between consecutive
values of the angle of mean electrical axis.
2. The method for measuring body functions of claim 1, further
comprising the step of visually scoring the EDR signal for apneic
and hypopneic events.
3. The method for measuring body functions of claim 1, wherein the
step of locating QRS pulses in part (c) filter comprises the
following steps:
(a) plotting suitable ECG data representing measurements of an
amplitude of the ECG signals in volts as a function of time to form
a data plot;
(b) applying a smoothing filter to the data plot to obtain smoothed
data;
(c) applying a low pass filter to the data plot to obtain low pass
filtered data;
(d) calculating a squared difference between the smoothed data and
the low pass filtered data to obtain a squared difference data;
(e) filtering the squared difference data to obtain a filtered
squared difference data; and
wherein locations of peaks in the filtered squared difference data
represent where the QRS pulses are located.
4. The method of measuring body functions of claim 1, wherein the
modeling of the EDR signal is performed with a cubic spline
interpolation technique.
5. The method of measuring body functions of claim 1, wherein the
step of calculating the area of the QRS pulses is by computing a
sum of areas of rectangles represented by a sample period and each
sample value with a QRS pulse period.
6. The method of measuring body functions of claim 1, wherein the
pre-determined locations for electrode placement comprise an
intercostal space to the right of a sternum as a V1 electrode,
close to an upper left arm as an LA electrode, close to an upper
right arm as an RA electrode, and close to a lower left rib as an
LL electrode.
7. The method of measuring body functions of claim 1, wherein the
step of calculating the angle of mean electrical axis comprises the
following steps:
(a) dividing the area of the QRS pulses as calculated from the ECG
signal measurements taken on the first lead by the area of the QRS
pulse as calculated from the ECG signal measurements taken on the
second lead and obtaining a result; and
(b) calculating an inverse tangent of the result.
8. The method of measuring body functions of claim 6, wherein the
first lead is measured from the RA electrode to the LA electrode
and the second lead is measured from the RA electrode to the LL
electrode.
9. The method of measuring body functions of claim 6, wherein the
first lead is measured from the RA electrode to the LL electrode
and the second lead is measured from the LA electrode to the V1
electrode.
10. The method of measuring body functions of claim 9, wherein the
RA electrode and the LL electrode comprise using only positive and
negative electrodes.
11. The method of measuring body functions of claim 1, further
comprising the step of comparing the EDR signals with measured
airflow signals in a patient.
12. The method of measuring body functions of claim 1, further
comprising measuring additional ECG signals with a conventional
polygraphic monitoring system.
13. A method for measuring body functions, comprising the following
steps:
(a) placing ECG electrodes on a patient's body at an intercostal
space to the right of a sternum as an V1 electrode, close to an
upper left arm as an LA electrode, close to an upper right arm as
an RA electrode, and close to a lower left rib as an LL
electrode;
(b) measuring ECG signals in two planes, as a first lead and a
second lead;
(c) locating QRS pulses within measured ECG signals;
(d) calculating an area of the QRS pulses from measured values of
the ECG signals;
(e) dividing the area of the QRS pulses as calculated from the ECG
signal measurements taken on the first lead by the area of the QRS
pulse as calculated from the ECG signal measurements taken on the
second lead and obtaining a result; then
(f) calculating an inverse tangent of the result to determine an
angle of mean electrical axis;
(g) modeling an EDR signal by interpolation between consecutive
values of the angle of mean electrical axis; and
(h) visually scoring the EDR signal for apneic and hypopneic
events.
14. The method of measuring body functions of claim 13, wherein the
first lead is measured from the RA electrode to the LA electrode
and the second lead is measured from the RA electrode to the LL
electrode.
15. The method of measuring body functions of claim 13, wherein the
first lead is measured from the RA electrode to the LL electrode
and the second lead is measured from the LA electrode to the V1
electrode.
16. The method of measuring body functions of claim 13, wherein the
modeling of the EDR signal is performed with a cubic spline
interpolation technique.
17. A method for measuring body functions, comprising the following
steps:
(a) placing ECG electrodes on a patient's body at an intercostal
space to the right of a sternum as an V1 electrode, close to an
upper left arm as an LA electrode, close to an upper right arm as
an RA electrode, and close to a lower left rib as an LL
electrode;
(b) measuring ECG signals in two planes, as a first lead and a
second lead;
(c) locating QRS pulses within measured ECG signals;
(d) calculating an area of the QRS pulses from measured values of
the ECG signals;
(e) dividing the area of the QRS pulses as calculated from the ECG
signal measurements taken on the first lead by the area of the QRS
pulse as calculated from the ECG signal measurements taken on the
second lead and obtaining a result; then
(f) calculating an inverse tangent of the result to determine an
angle of mean electrical axis;
(g) modeling an EDR signal by interpolation between consecutive
values of the angle of mean electrical axis;
(h) visually scoring the EDR signal for apneic and hypopneic
events; and
(i) comparing the EDR signals with measured airflow signals in a
patient.
18. The method of measuring body functions of claim 17, wherein the
first lead is measured from the RA electrode to the LA electrode
and the second lead is measured from the RA electrode to the LL
electrode.
19. The method of measuring body functions of claim 17, wherein the
first lead is measured from the RA electrode to the LL electrode
and the second lead is measured from the LA electrode to the V1
electrode.
Description
BACKGROUND OF THE INVENTION
1. Technical Field
The present invention relates generally to diagnosis of sleep
disordered breathing and in particular to diagnosis of sleep
disordered breathing utilizing electrocardiographic measurements.
Still more particularly, the present invention relates to
derivation of respiratory data from electrocardiographic
measurements for determining either the presence of sleep
disordered breathing causing or aggravating cardiac symptoms or the
absence of sleep disordered breathing influence on cardiac symptoms
due to cardiac pathology.
2. Description of the Related Art
Sleep disordered breathing is a significant problem for a large
portion of the population. Sleep apnea, an intrinsic dyssomnia
involving cessation of breathing during sleep and resulting in
complete or partial arousal from sleep, is one of the most
prevalent forms of sleep disordered breathing. Symptoms of the
disorder include daytime sleepiness, fatigue or tiredness, and
irritability, which may seriously impair the performance of the
individual.
Sleep apnea is typically defined as the cessation of air exchange
(breathing) from the nostrils or mouth lasting at least 10 seconds.
Partial or complete arousal from sleep is considered a defensive
mechanism most likely stimulated by rising carbon dioxide levels in
the blood during the apneic event to reestablish ventilation and
prevent death in the sleeping subject. Three established
categorized of sleep apnea include: obstructive sleep apnea,
obstruction of the upper airway; central sleep apnea, cessation of
ventilatory effort; and mixed apnea, a combination of both upper
airway obstruction and cessation of ventilatory effort.
Sleep disordered breathing may also take the form of a decrease in
ventilation during sleep rather than a complete sleep apnea, which
may result in hypercarpnea and sleep disturbance and is classified
as hypopnea. Apneic events are usually quantified (or "scored") as
either (1) more than 75% reduction in air flow, with or without
change in oxygen saturation in the blood (SpO.sub.2), or (2) more
than 50% reduction in air flow combined with a decrease of blood
oxygen saturation by more than 10%. Hypopneic events may be
variously quantified as: (1) either a 50% or greater reduction in
air flow combined with at least a 4% reduction in blood oxygen
saturation or, alternatively, a 20%-50% reduction in airflow in
association with at least 2% loss of blood oxygen saturation; (2) a
50% or greater reduction in thoracic and abdominal activity; (3) a
change in electromyogram (EMG) measurements accompanied by, rolling
eye movements, indicating arousal; and/or (4) a change in
electroencephalogram (EEG) measurements combined with a 20%
decrease in air flow, independent of decrease in blood oxygen
saturation.
While the distinction between apnea and hypopnea is largely one of
severity, sleep disordered breathing diagnosis may entail
measurement of both types of events. For example, the
apnea-hypopnea index (AHI), representing a number of either apneic
or hypopneic events per hour for a subject, is more commonly used
than the apnea index (AI), representing only the total number of
apneic events per hour for the subject. An AHI of more than 5
events per hour, regardless of severity, is usually qualified as
sleep apnea. Other variables such as average duration of an event,
number of apneic versus hypopneic events, and average decrease in
blood oxygen saturation during events are utilized to determine the
severity of the disorder.
Polygraphic monitoring, or polysomnography, the measurement of
vital body signals during sleep, is the most commonly employed
method of diagnosing sleep disorders, including sleep apnea. The
data is collected during the patient's normal sleeping time and is
later scored (evaluated) visually. Various signals are recorded
during the night to identify different sleep stages, respiratory
variables, heart function, and muscle tone, all of which aid in
scoring sleep disordered breathing events.
A conventional arrangement of the polygraphic monitoring
instrumentation employed is depicted in FIG. 8. A polygraphic
monitoring unit 802 is connected by a plurality of leads 804 to
sensors attached to patient 806. Polygraphic monitoring unit 802 is
capable of measuring a variety of body functions:
electroencephalogram (EEG) lead 804a is employed to measure
electrical brain activity; electrooculogram (EOG) lead 804b is
employed to detect eye movements; airflow lead or leads 804c are
employed to measure air flow signals from as many as three
thermistors placed near the patient's nostrils and mouth;
electromyogram (EMG) lead 804d is employed to measure muscle tone
from the patient's chin area; electrocardiogram (ECG) leads 804e
are employed to measure the heart function; and chest and abdominal
band leads 804f and 804g are employed to measure thoracic and
abdominal movements, respectively. Additionally, a pulse oximeter
(not shown) may be employed to record blood oxygen saturation, an
electrode may be placed on the tibialis anterior to monitor leg
muscle activity, and a video recording of the patient may be taken
utilizing infrared low light technology.
Measurements taken from polygraphic monitoring unit 802 are
typically filtered and amplified, and recorded on a data
acquisition system 808 such as those available from Tele-factor
Corporation of West Conshohocken, Pa. The polysomnography signals
are also usually digitized by an analog-to-digital converter 810
and transmitted to a data processing system 812 for processing
and/or storage. Converter 810 may, for example, be a DAS 1200
series A/D converter board, available from Keithly Instruments,
Inc. of Massachusetts, within data processing system 812.
Conventional polygraphic monitoring instrumentation is often
uncomfortable to the patient. The instrumentation also embraces
several forms of respiration monitoring. Currently, two broad
categories of respiration monitoring may be identified: direct
methods, such as nasal thermistors, spirometers, and
pneumotachometers, measure air flow in and out of the lungs;
indirect methods, which presently include whole body
plethysmographs, inductance and impedance plethysmographs, and
strain gauge measurement of chest and abdomen circumference,
measure effects of respiration on the body. While direct methods
are most accurate, they generally interfere with normal
respiration. Most indirect methods, on the other hand, either lose
their calibration readily or immobilize the patient (e.g., whole
body plethysmograph).
Sleep disordered breathing is prevalent in individuals suffering
from cardiovascular disease. ECG signals are routinely recorded in
studies for patients with cardiac problems, as well as in patients
having respiratory disorders, sleep disorders, and patients in
intensive care units. ECG signals are therefore readily available
for patients having a variety of disorders. Furthermore, millions
of patients are screened each year using extended ECG monitoring
(at least 24 hours), while generally their respiration is not
monitoring due to the added cost and inconvenience of conventional
airflow monitoring equipment.
Advances in the field of electrocardiography have rendered analysis
and conditioning or ECG signals robust. Measurement of ECG signals
does not interfere with normal breathing. Established technology
has existed for years for measurement of the ECG in ambulatory
patients. Thus, measurement of ECG signals is more comfortable and
less intrusive for the patient than polygraphic monitoring.
Properly attached ECG leads are less prone to error due to patient
movement.
It is well-known that respiration affects ECG signals, principally
as a result of chest movement. Much work has been performed to
eliminate this effect from ECG signals to enhance detection of
arrhythmia. Capture of chest movement induced modulation of ECG
signals, however, could provide a means for extracting respiratory
rhythms from ECG signals.
Derivation of respiratory rhythms from ECG signals would not
require supplementary sensors. ECG-derived respiratory (EDR)
signals could also provide valuable clinical information if they
reveal an association between abnormal respiratory and cardiac
events. Presently, however, no ECG monitoring device provides
information regarding the patient's breathing function. Thus,
respiratory influence on cardiac function is not available to the
attending physician when making an ECG-based diagnosis.
It would be desirable, therefore, to provide a mechanism for
deriving respiratory data from ECG measurements. It would further
be advantageous if the mechanism for generating EDR signals could
be implemented in connection with a variety of mechanisms utilize
to diagnose cardiac, respiratory, or sleep disorders.
SUMMARY OF THE INVENTION
Respiratory rhythms of a subject are derived from measured ECG
signals utilizing leads placed for significant influence of chest
movement on the ECG signals. The QRS pulses within ECG signals
measured in two substantially orthogonal planes are located by
applying a sequence of filters. The knot and period of the QRS
pulses are then determined, with adjustments made during a learning
phase of data sampling. The QRS pulse areas in both planes is then
calculated. These pulse areas are employed to determine the angle
of orientation of the depolarization wave's mean electrical axis
(MEA) at the QRS pulse locations. Cubic spline interpolation of the
data points for the MEA angle provides a smooth breathing curve,
which may be scored for sleep disordered breathing events. The
ECG-derived respiratory (EDR) signal may be employed in lieu of
airflow a measurements where such measurements are not available,
or may be employed in conjunction with airflow measurements and/or
measured cardiac activity data to discriminate between arrhythmias
associated with disordered breathing versus those associated with
cardiac malfunction, reducing misdiagnosis. The additional
processing of ECG data required for derivation of respiratory
rhythms may be easily automated and implemented at nominal
incremental cost per unit.
BRIEF DESCRIPTION OF THE DRAWINGS
The novel features believed characteristic of the invention are set
forth in the appended claims. The invention itself, however, as
well as a preferred mode of use, further objectives and advantages
thereof, will best be understood by reference to the following
detailed description of an illustrative embodiment when read in
conjunction with the accompanying drawings, wherein:
FIG. 1 depicts a high level flowchart for a process of deriving
respiratory rhythms from ECG signals in accordance with a preferred
embodiment of the present invention;
FIGS. 2A-2C are diagrams of ECG signals and their components, their
projections onto various measurement planes, and the components of
measured ECG signals;
FIG. 3 depicts a diagram of electrode placement for ECG measurement
in deriving respiratory rhythms from ECG signals in accordance with
a preferred embodiment of the present invention; and
FIG. 4 is a high level flowchart for a process of detecting QRS
pulse locations within measured data from an ECG signal in
accordance with a preferred embodiment of the present
invention;
FIGS. 5A-5E are data plots for a process of detecting QRS pulse
locations within measured data in accordance with a preferred
embodiment of the present invention;
FIGS. 6A-6D depict data plots for measured and derived data
utilizing the process for derivation of respiratory rhythms from
ECG signals in accordance with a preferred embodiment of the
present invention;
FIG. 7 is a comparative data plot for a measured airflow and a
ECG-derived respiratory rhythm signal in accordance with a
preferred embodiment of the present invention; and
FIG. 8 depicts a conventional arrangement of the polygraphic
monitoring instrumentation, in which a preferred embodiment of the
present invention may optionally be implemented.
DETAILED DESCRIPTION
With reference now to the figures, and in particular with reference
to FIG. 1, a high level flowchart for a process of deriving
respiratory rhythms from ECG signals in accordance with a preferred
embodiment, of the present invention is depicted. The process
begins at step 102, which depicts receiving ECG measurement data
taken from two measurement planes utilizing leads between
selectively placed transducer electrodes attached to the
patient.
Derivation of respiratory rhythms from ECG signals depends on how
the ECG signal components are measured. The usefulness of ECG
signal component measurements in deriving respiratory rhythms may
be maximized by careful placement of the leads, taking into account
the patient's position (i.e., erect or supine).
Referring to FIGS. 2A-2C, diagrams of ECG signals and their
components, their projections onto various measurement planes, and
the components of measured. ECG signals are illustrated.
Electrocardiography is based on the principle that current flow in
the extracellular fluid is proportional to the rate of change of
action potential in the myocardial cells of the heart. A plateau in
the action potential, produces a negligible current in the
extracellular fluid, while repolarization and depolarization
produce high currents.
The mechanism of electrical conductivity in the body involves ionic
currents (ions as charge carriers). Bipotential body surface
electrodes employed in electrocardiography transduce those ionic
currents into electric currents, making it possible to record the
bioelectric activity of the heart. Repolarization and
depolarization in the myocardium may thus be seen as discrete
events in the electrical signal transduced by these surface
electrodes placed on the thorax. A recording of the electrical
signal is referred to as an electrocardiogram.
ECG signals are conventionally characterized as including the P-,
Q-, R-, and S-wave components illustrated in FIG. 2A. These
components correspond to movement of the depolarization wave
through the heart, causing contraction. The area of the QRS complex
within the ECG signal is typically a factor of interest.
In a normal heart, the total cycle of the PQRS (depolarization
wave) starts at the SA node (generator) and travels away from that
node through parts of the atria, causing the P-wave. Next, the AV
node receives impulses from the SA node and also from the
sympathetic and parasympathetic nervous system.
After passing through the AV node, the depolarization wave spread
rapidly along the ventricular septum and to the left and right
ventricles, thus causing a contraction. The QRS complex of interest
in the present invention is recorded during this phase of rapid
ventricular depolarization. The path of the depolarization wave
around the heart is illustrated in FIG. 2B. In the intact heart,
the instantaneous depolarization vector changes continuously as the
depolarization wave flows through the heart. The vectorial sum of
all instantaneous vectors is referred to as the mean electrical
axis (MEA).
The direction of the flow of the depolarization wave in three
dimensional space gives the direction of the MEA. As illustrated in
FIG. 2B, projections of the depolarization wave path may be
measured in various planes: the frontal (x-y) plane, the transverse
(x-z) plane, and the sagittal (y-z) plane (where the x axis is
oriented across the front of the patient's body and the y axis is
oriented along the length of the patient's body). The position of
the electrodes on the thorax determines the polarity and amplitude
of the ECG events; that is, the position of the leads determine the
plane in which the depolarization wave signal is measured.
In practice, x and y axis leads are utilized to find the
orientation of the MEA in two dimensions, along the frontal and
transverse planes as illustrated in FIG. 2C. Data is not normally
collected along the sagittal plane for routine ECG monitoring. The
area of the QRS complex in the frontal plane (A.sub.y) and the area
of the QRS complex in the transverse plane (A.sub.x) are then
utilized to determine the orientation (.theta.) of the MEA in the
x-y plane.
Referring now to FIG. 3, a diagram of electrode placement for ECG
measurement in deriving respiratory rhythms from ECG signals in
accordance with a preferred embodiment of the present invention is
depicted. Respiration affects ECG signals both through a low
frequency baseline shift, which is routinely filtered as baseline
noise, and by amplitude modulation of the QRS pulses, primarily due
to chest movement, which changes the position of the leads relative
to the heart. Amplitude modulation of QRS pulse measurements
results in a change of the area of the QRS pulses measured in
different planes.
Leads L1, L2, and L3 depicted in FIG. 3 are three standard frontal
plane bipolar leads (vectors) measured from electrodes placed on
the patient's thorax as shown. With some individual variation due
to heart orientation and conductivity/resistivity, the orientation
of the MEA in the frontal plane is normally along lead L2, which
therefore records the largest signal amplitude. A modified L2 lead,
which employs only positive and negative electrodes (RA and LL)
rather than standard positive, negative, and reference electrodes
(RA, LL and LA), is preferably employed in the present
invention.
Normally, an electrode is also placed on the patient's back to
obtain transverse plane measurements. Since the back electrode can
be uncomfortable for the patient and noisy due to patient movement,
a modified transverse plane lead MCL.sub.1 is preferably employed
in the present invention to make horizontal plane measurements.
Electrode V.sub.1, placed at the fourth intercostal space to the
right of the sternum, is utilized as one electrode for lead
MCL.sub.1, while electrode LA close to the upper left arm is
utilized as the second electrode. Only the relative amplitude of
the resultant ECG signal changes. Measurements from leads L2 and
MCL.sub.1 thus provide components of the area of the QRS complex in
two orthogonal planes, with lead L2 preferably designated for
measuring A.sub.y and lead MCL.sub.1 designated for measuring
A.sub.x.
In erect patients, expansion and contraction of the chest during
respiration takes place in both the horizontal (within the
transverse plane due to expansion and contraction of the chest) and
vertical (within the frontal plane due to diaphragmatic movement)
directions. Diaphragmatic activity and the intercostal muscles
cause expansion and contraction in three dimensional space, a
periodic or a periodic movement which effects the ECG signals in
each lead differently. Thus, for erect patients, lead L1 will be
most effected by the horizontal component of chest movement during
respiration and lead L3 will be most affected by the vertical
component of thoracic movement, while lead L2 will be least
affected. Thus, for an erect patient, the respiratory signal may be
derived from measurement on lead L1 of A.sub.x, the horizontal area
component of the QRS pulse, and on lead L3 of A.sub.y, the vertical
area component.
The derivation of the EDR signal from ECG measurements is the same
for either pair of leads--L1 and L3 or modified L2 and MCL.sub.1
--and may be applied separately to both pairs of leads. ECG signals
are preferably sampled from the selected lead pair at 500 Hz.
Referring again to FIG. 1, upon receiving ECG measurements from a
selected lead pair as depicted in step 102, the process passes to
step 104, which illustrate locating QRS pulses within the measured
ECG data. To be able to calculate the area of the QRS complex, it
is necessary to have a robust process for accurately detecting the
correct location of the QRS complex even under noisy conditions. A
number of QRS detection algorithms are known in the art. However,
the process of QRS location detection in the present invention has
a high accuracy of detection and low noise sensitivity, resulting
in fewer false positives and fewer false negatives.
Referring to FIGS. 4 and 5A-5E, which are intended to be considered
together, a high level flowchart and corresponding data plots for a
process of detecting QRS pulse locations within measured data in
accordance with a preferred embodiment of the present invention are
illustrated. The process depicted in FIG. 4 begins at step 402,
which depicts receiving raw ECG data from which QRS pulse locations
are to be determined. A data plot of suitable raw data X,
representing (possibly amplified) measurements of the amplitude an
ECG signal in volts as a function of time, is shown in FIG. 5A.
The process of FIG. 4 then passes to step 404, which illustrates
applying a smoothing filter to reduce noise. A suitable smoothing
filter may be emulated by calculating: ##EQU1##
where Y1 represents the smoothed data. The data plot shown in FIG.
5A smoothed using the filter modeled by equation (1) is shown in
FIG. 5B.
From step 404, the process of FIG. 4 next passes to step 406, which
depicts applying a low pass filter to the smoothed data, modeled by
the computation of: ##EQU2##
where Y2 represents the low pass filtered data and m=2. A data plot
of the smoothed data in FIG. 5B after a low pass filter is applied
as described is shown in FIG. 5C.
From step 406, the process of FIG. 4 then passes to step 408, which
illustrates computing a squared difference between the smoothed
data resulting from step 404 and the low pass filtered, smoothed
data resulting from step 406. The squared difference is computed
by:
where Y3 represents the computed squared difference. A data plot
for the squared difference between the smoothed data in FIG. 5B and
the low pass filtered, smoothed data shown in FIG. 5C is shown in
FIG. 5D. Sharp peaks corresponding to the location of QRS pulses
appear after calculating the squared difference, because the
squared difference between Y1 and Y2 enhances the high frequency
components present in the QRS pulse while removing the lower
frequency components.
From step 408, the process of FIG. 4 next passes to step 410, which
depicts low pass filtering the squared difference data again to cut
off higher frequencies, utilizing a filter which may be modelled
by: ##EQU3##
where Y4 represents the low pass filtered, squared difference data
signal and m=2 is again selected. A plot of the data in FIG. 5D
which has been low passed filtered with equation (4) is shown in
FIG. 5E. The locations of the peaks in this filtered signal Y4
represent the actual QRS pulse locations. An array of pulse
location may thus be generated for further processing efforts.
Referring once again to FIG. 1, from step 104 the process next
passes to step 106, which depicts calculating the area of the QRS
pulses which have been located within the raw ECG data. For this
step, the R-wave component of each QRS pulse within the ECG signal
is first located by searching for it in the vicinity of the
detected QRS pulse location. Once the R-wave component is
identified, the position of the knot (start) and end of the QRS
pulse are established. The knot and end positions within the QRS
pulse may be identified utilizing suitable zero-crossing detection
algorithms known in the art.
The position of the knot, and thus the area of the QRS pulse,
varies to a small extent from subject to subject. Accordingly, the
first twelve seconds worth of raw ECG data is utilized as a
"learning phase" to adjust the knot position and period of the QRS
pulse for a particular subject, which are then utilized in
computing the area of the QRS pulse. The twelve second learning
phase period was selected by trial and error, determined to be
sufficiently long to ascertain the subject-specific knot position
and pulse period to an acceptable accuracy.
By trial and error, the knot is placed between 40 to 60 msec before
the R-wave component of each QRS pulse during the learning phase.
The area of each QRS pulse is calculated starting from the knot
over the next approximately 100 to 150 msec, a period which is
adjusted during the learning depending on the subject-specific
period of the QRS pulse. The area of the digitized QRS pulse data
may be calculated to a reasonable approximation by computing the
sum of the areas of rectangles represented by the sample period and
each sample value within the QRS pulse period. The area of the QRS
pulse is separately calculated for each lead within a selected pair
(L1 and L3 or modified L2 and MCL.sub.1), and the location of the
QRS pulse for data measured from each lead is noted.
The process then passes to step 108, which illustrates calculating
the angle of the mean electrical axis for each QRS pulse from:
##EQU4##
where .theta..sub.MEA (or simply .theta.) is the angle of
orientation of the MEA, A.sub.x is the corresponding QRS pulse area
as calculated from the measurements taken on one lead (L1 or
MCL.sub.1), and A.sub.y is the corresponding QRS pulse area as
calculated from the measurements taken on the other lead (L3 or
modified L2). The MEA orientation values are then stored in
association with the location of the QRS pulse.
The process, next passes to step 110, which depicts construction of
the ECG-derived respiratory (EDR) signal by interpolation between
consecutive values of the MEA orientation. A cubic spline
interpolation is suitable for generating the EDR signal from the
computed MEA orientation data points, generating a smooth breath
signal.
Cubic spline interpolation, a technique known in the art, utilizes
three consecutive original data points to calculate the
coefficients in a cubic equation for a curve passing through those
points. The resulting cubic equation is then employed to generate
additional data points between the original data points. Thus a
series of cubic equations, each having (potentially) different
coefficients, is utilized to interpolate the MEA orientation data
derived from the raw ECG signals described above.
The cubic equation, obtained by taking the first four terms of the
Maclaurin series, is represented as: ##EQU5##
where t is time, .theta..sub.k (0) is the initial value
coefficient, .theta..sub.k '(0) is the first differential
coefficient, .theta..sub.k "(0) is the second differential
coefficient, and .theta..sub.k '"(0) is the third is differential
coefficient of the cubic equation represented by .theta..sub.k (t).
Equation (6) may be differentiated to obtain:
If .theta..sub.k, .theta..sub.k+1, and .theta..sub.k+2 are three
points obtained by calculating the angle of MEA and T.sub.1 is the
time between .theta..sub.k and .theta..sub.k+1 while T.sub.2 is the
time between .theta..sub.k-1 and .theta..sub.k+2, then by assuming
that at t=0, .theta..sub.k (0)=.theta..sub.k and .theta..sub.k
'(0)=.theta..sub.k '=(.theta..sub.k+1 -.theta..sub.k)/T.sub.1 and
forcing the equation to pass through .theta..sub.k+1, at t=T.sub.1,
.theta..sub.k (T.sub.1)=.theta..sub.k+1. The first differential may
be written as .theta..sub.k '(T.sub.1)=(.theta..sub.k+2
-.theta..sub.k)/T.sub.2.
From equation (6) at t=T.sub.1 we obtain ##EQU6##
and at t=T.sub.2 we obtain ##EQU7##
Therefore, from equations (8) and (9): ##EQU8##
and ##EQU9##
If there are 0 to n number of points in the signal to be
interpolated, then k varies from 0 to n-2 and k number of equations
are generated.
The cubic spline interpolation technique described above may be
utilized to represent the EDR signal with a sampling rate of 10 Hz.
The process then passes to step 112, which illustrates the process
becoming idle until subsequent ECG data is provided for derivation
of respiratory rhythms in accordance with the present invention.
The EDR signal may then be visually scored as a respiratory signal
for apneic and hypopneic events in accordance with the known art,
or evaluated utilizing different methods.
With reference now to FIGS. 6A-6D, data plots for measured and
derived data utilizing the process for derivation of respiratory
rhythms from ECG signals in accordance with a preferred embodiment
of the present invention are depicted. FIGS. 6A and 6B depict data
plots of measured ECG signals from leads modified L2 and MCL.sub.1
respectively. FIG. 6C depicts a data plot of computed MEA
orientation angles for QRS pulses within the measured ECG signals
of FIGS. 6A and 6B. Finally, FIG. 6D depicts a data plot of an EDR
signal generated by a cubic spline interpolation of the data points
depicted in FIG. 6C.
Referring to FIG. 7, a comparative data plot for a measured airflow
and a ECG-derived respiratory rhythm signal in accordance with a
preferred embodiment of the present invention is illustrated. The
present invention may be implemented within a conventional
polygraphic monitoring system of the type depicted in FIG. 8. Such
an implementation is useful in comparing EDR signals to measured
airflow signals to determine how well the EDR signals reflect the
lack of air exchange. Subjects may be if asked to simulate
obstructive and central apnea prior to the start of a sleep period
to provide comparison ECG data when no respiration is present.
It is known that apnea episodes may be associated with large
fluctuations in cardiac rates, bradycardia during the apnea, and
tachycardia during recovery from an episode in sleep. Increased
ventricular ectopy and asystole have also been noted in obstructive
sleep apnea patients. Therefore, some positive ECG recordings
contain significant cardiac irregularities due to sleep disordered
breathing rather than a primary cardiac pathology. Simultaneous
examination of measured respiratory data and EDR signals utilizing
the present invention within a system of the type depicted in FIG.
8 will allow more accurate identification of sleep-disordered
breathing events and permit determination of the cause of positive
ECG.
In FIG. 7, measured airflow sampled at 10 Hz and normalized is
compared to EDR signals generated for corresponding ECG data. A
high correlation is shown between measured airflow patterns and
ECG-derived respiratory rhythms, demonstrating the utility of the
present invention. ECG data sampled at 500 HZ was processed to
generate an EDR signal of respiration sampled at 10 Hz for
comparison.
The availability of both measured and derived respiratory data
together with cardiac activity data provides the attending
cardiologist with the necessary information to discriminated
between arrhythmias associated with disordered breathing versus
those associated with intrinsic cardiac malfunction. Misdiagnosis
of arrhythmias caused by respiratory function can, in extreme
cases, lead to expensive and inappropriate treatment such as
implanting a heart pacemaker. The present invention may
significantly reduce the incidence of such events. No additional
equipment, beyond the required data processing software, is
required to derive respiratory rhythms from ECG signals.
Furthermore, processing may be easily automated and combined with
routine processing of extended ECG data with nominal increase in
unit costs.
It is important to note that while the present invention has been
described in the context of a fully functional electrocardiographic
device and/or data processing system, those skilled in the art will
appreciate that the mechanism of the present invention is capable
of being distributed in the form of a computer readable medium of
instructions in a variety of forms, and that the present invention
applies equally regardless of the particular type of signal bearing
media used to actually carry out the distribution. Examples of
computer readable media include: magnetic tape, recordable type
media such as floppy disks and CD-ROMs and transmission type media
such as digital and analog communication links.
The description of the preferred embodiment of the present
invention has been presented for purposes of illustration and
description, but is not intended to be exhaustive or limit the
invention in the form disclosed. Many modifications and variations
will be apparent to those of ordinary skill in the art. The
embodiment was chosen and described in order to best explain the
principles of the invention and the practical application to enable
others of ordinary skill in the art to understand the invention for
various embodiments with various modifications as are suited to the
particular use contemplated.
* * * * *